Metaheuristics for data mining

Dhaenens, Clarisse; Jourdan, Laëtitia

doi:10.1007/s10288-019-00402-4

Cited by 12 publications

(3 citation statements)

References 129 publications

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“…The biclustering problem aims to extract biclusters of a maximal size that satisfy a coherence constraint. This task of extracting bicluster from a data matrix can be seen as a combinatorial optimization problem [15,16].…”

Section: Biclustering As An Optimization Problemmentioning

confidence: 99%

Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

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Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant biclusters is an NP-hard problem that can be formulated as an optimization problem. Therefore, different metaheuristics have been applied to biclustering problems because of their exploratory capability of solving complex optimization problems in reasonable computation time. Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics. This chapter will present a survey of metaheuristics approaches to address the biclustering problem. The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators. A specific discussion on single versus multi-objective approaches is presented. Finally, some emerging research directions are presented.

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Section: Biclustering As An Optimization Problemmentioning

confidence: 99%

Biclustering Algorithms Based on Metaheuristics: A Review

José-García¹,

Jacques²,

Sobanski³

et al. 2022

Preprint

Self Cite

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“…Said that, clustering and CRP problems share several features that make (meta‐)heuristic algorithms such as the ones presented in Dhaenens and Jourdan (2016, 2019), and José‐García and Gómez‐Flores (2016) worthy to be studied and adapted to the CRP. In particular, local search algorithms have also been proposed to clustering problems (Fränti and Kivijärvi, 2000; Deng and Bard, 2011; Hiep et al., 2016).…”

Section: Composite Retrieval Problemmentioning

confidence: 99%

Local search algorithms for the composite retrieval problem

Moyano

Zabala

Gatica

et al. 2022

Int Trans Operational Res

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In this paper, we address a problem arising in information retrieval (IR) called composite retrieval problem (CRP) of diverse and complementary bundles. The CRP aims to group items into bundles and then select a subset of such bundles, so that we can maximise the similarity of the items within a bundle and, simultaneously, can maximise the complementarity of the selected bundles. To this end, the CRP approach considers the existing relations among items' attributes, leading to the selection of bundles that satisfy users' expectations without the needing for any refining query and, thus, improving the searching experience, with respect to traditional IR approaches. In this study, we propose three efficient yet straightforward algorithms, namely Local Search, Iterative Local Search and Variable Neighbourhood Search. Further, two different neighbourhood moves are evaluated at each algorithm. Although the first neighbourhood move is focused on the exploitation of the nearby search space, the second one is focused on the exploration of larger portions of the search space. All these algorithms are applied to two real-world publicly available instances and compared to the state-of-the-art algorithms in CRP. Obtained results suggest that combining both neighbourhood moves leads to better results in terms of both, the complementarity of the bundles and similarity of the items. Further, results show that, after statistical analysis, the proposed algorithms are significantly better, for the vast majority of the experiments performed in this study, when compared to the state-of-the-art algorithms in CRP.

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“…These features have made them popular in recent decades. Also, these types of algorithms are among the most popular techniques that are employed for solving optimization problems in different fields, such as computer and electrical engineering [ 7 ], water, geotechnical and transport engineering [ 8 ], structure and infrastructures engineering [ 9 ], robotic [ 10 ], project and construction management [ 11 ], feature selection and data mining [ 12 , 13 ], industrial and manufacturing [ 14 ], and medicine and biology [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Social Network Search for Solving Engineering Optimization Problems

Bayzidi

Talatahari

Saraee

et al. 2021

Computational Intelligence and Neuroscience

155

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In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

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Metaheuristics for data mining

Cited by 12 publications

References 129 publications

Biclustering Algorithms Based on Metaheuristics: A Review

Biclustering Algorithms Based on Metaheuristics: A Review

Local search algorithms for the composite retrieval problem

Social Network Search for Solving Engineering Optimization Problems

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